Population pharmacokinetics of liposomal irinotecan in patients with cancer and exposure-safety analyses in patients with metastatic pancreatic cancer.

Liposomal irinotecan is a liposomal formulation of irinotecan, which prolongs circulation of irinotecan and its active metabolite SN-38. A population pharmacokinetic (PK) model was developed based on data from seven studies (N = 440). Adequacy of the model was assessed using multiple methods, including visual predictive check. Associations between PK exposure and the incidence of diarrhea (grade ≥3) and neutropenia adverse events (AEs) (grade ≥3) at first event in patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) were investigated using logistic regression based on data from two studies (the phase III NAPOLI-1 [N = 260] and phase I/II NCT02551991 [N = 56] trials). The PKs of total irinotecan was described by a two-compartment model with first-order elimination, with SN-38 formed directly by a first-order constant from the central compartment of irinotecan or after using a transit compartment. Clearance was 17.9 L/week (0.107 L/h) and 19,800 L/week (118 L/h) for total irinotecan and SN-38, respectively. The UGT1A1*28 7/7 homozygous genotype had no significant impact on SN-38 clearance. Model evaluation was satisfactory for both irinotecan and SN-38. The incidence of diarrhea (grade ≥3) at first event was significantly higher with increasing average concentrations of total irinotecan and SN-38; there was no significant association between an increased risk of neutropenia AEs (grade ≥3) at first event and average SN-38 concentrations. In summary, the PKs of total irinotecan and SN-38 after administration of liposomal irinotecan were well-described by the model. The UGT1A1*28 status had no significant impact on the PKs of liposomal irinotecan.

First-in-human phase I study of oral S49076, a unique MET/AXL/FGFR inhibitor, in advanced solid tumours.

BACKGROUND AND OBJECTIVES: S49076 is a novel ATP-competitive tyrosine kinase inhibitor of MET, AXL and FGFR with a unique selectivity profile. A phase I open-label study was undertaken to establish the tolerability profile and determine the recommended dose (RD) and administration schedule. MATERIALS AND METHODS: Patients with advanced solid tumours received S49076 orally once-daily (qd) or twice-daily (bid) in continuous 21-day cycles at escalating doses guided by a 3 + 3 design and followed by an expansion phase at the RD. Pharmacokinetic (PK) parameters were assessed and pharmacodynamic end-points were evaluated in pre- and post-treatment tumour biopsies. Preliminary anti-tumour activity was evaluated as per the Response Evaluation Criteria In Solid Tumours 1.1 criteria. RESULTS: A total of 103 patients were treated: 79 in the dose-escalation and 24 in the expansion. Doses from 15 to 900 mg were evaluated. Dose-limiting toxicities were reported in 9 patients and occurred at 30, 760 and 900 mg in the qd arm and at 180, 225 and 285 mg in the bid arm. The RD was defined at 600 mg qd. Adverse events (AEs) occurred with similar frequency in both regimens at an equivalent total daily dose. Overall, 83 patients (81.4%) had drug-related AEs, the majority (93%) of which were grade I-II (National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0) and only 3% led to drug discontinuation. Intratumoural PK analysis at the RD suggested hitting of MET, AXL and FGFR. CONCLUSION: S49076 demonstrated a tolerable safety profile with limited single-agent activity. PK/pharmacodynamic readouts of S49076 are encouraging for further investigation of S49076 in combination therapies. TRIAL REGISTRATION NUMBER: ISRCTN00759419.

Model-based approaches for ivabradine development in paediatric population, part I: study preparation assessment.

The main objective was to help design a paediatric study for ivabradine, a compound already marketed in adults, focusing on: the paediatric formulation evaluation, the doses to be administered, the sampling design and the sampling technique. A secondary objective was to perform a comparison of the prediction of ivabradine pharmacokinetics (PK) in children using a physiologically-based pharmacokinetic (PBPK) approach and allometric scaling of a population pharmacokinetic (PPK) model. A study was conducted in order to assess the relative bioavailability (Frel) of the paediatric formulation and a similar Frel was observed between the paediatric formulation and the adult marketed tablet. PBPK modelling was used to predict initial doses to be administered in the paediatric study and to select the most appropriate sample time collections. The dried blood spot technique was recommended in the clinical trial in children. Simulations obtained by both the PBPK approach and allometric scaling of a PPK model were compared a posteriori to the paediatric study observations. Both PPK and PBPK approaches allowed an adequate prediction of the PK of ivabradine and its metabolite in children.

Optimal sampling times for a drug and its metabolite using SIMCYP(®) simulations as prior information.

BACKGROUND: Since 2007, it is mandatory for the pharmaceutical companies to submit a Paediatric Investigation Plan to the Paediatric Committee at the European Medicines Agency for any drug in development in adults, and it often leads to the need to conduct a pharmacokinetic study in children. Pharmacokinetic studies in children raise ethical and methodological issues. Because of limitation of sampling times, appropriate methods, such as the population approach, are necessary for analysis of the pharmacokinetic data. The choice of the pharmacokinetic sampling design has an important impact on the precision of population parameter estimates. Approaches for design evaluation and optimization based on the evaluation of the Fisher information matrix (M(F)) have been proposed and are now implemented in several software packages, such as PFIM in R. OBJECTIVES: The objectives of this work were to (1) develop a joint population pharmacokinetic model to describe the pharmacokinetic characteristics of a drug S and its active metabolite in children after intravenous drug administration from simulated plasma concentration-time data produced using physiologically based pharmacokinetic (PBPK) predictions; (2) optimize the pharmacokinetic sampling times for an upcoming clinical study using a multi-response design approach, considering clinical constraints; and (3) evaluate the resulting design taking data below the lower limit of quantification (BLQ) into account. METHODS: Plasma concentration-time profiles were simulated in children using a PBPK model previously developed with the software SIMCYP(®) for the parent drug and its active metabolite. Data were analysed using non-linear mixed-effect models with the software NONMEM(®), using a joint model for the parent drug and its metabolite. The population pharmacokinetic design, for the future study in 82 children from 2 to 18 years old, each receiving a single dose of the drug, was then optimized using PFIM, assuming identical times for parent and metabolite concentration measurements and considering clinical constraints. Design evaluation was based on the relative standard errors (RSEs) of the parameters of interest. In the final evaluation of the proposed design, an approach was used to assess the possible effect of BLQ concentrations on the design efficiency. This approach consists of rescaling the M(F), using, at each sampling time, the probability of observing a concentration BLQ computed from Monte-Carlo simulations. RESULTS: A joint pharmacokinetic model with three compartments for the parent drug and one for its active metabolite, with random effects on four parameters, was used to fit the simulated PBPK concentration-time data. A combined error model best described the residual variability. Parameters and dose were expressed per kilogram of bodyweight. Reaching a compromise between PFIM results and clinical constraints, the optimal design was composed of four samples at 0.1, 1.8, 5 and 10 h after drug injection. This design predicted RSE lower than 30 % for the four parameters of interest. For this design, rescaling M(F) for BLQ data had very little influence on predicted RSE. CONCLUSION: PFIM was a useful tool to find an optimal sampling design in children, considering clinical constraints. Even if it was not forecasted initially by the investigators, this approach showed that it was really necessary to include a late sampling time for all children. Moreover, we described an approach to evaluate designs assuming expected proportions of BLQ data are omitted.

Evaluation of different tests based on observations for external model evaluation of population analyses.

To evaluate by simulation the statistical properties of normalized prediction distribution errors (NPDE), prediction discrepancies (pd), standardized prediction errors (SPE), numerical predictive check (NPC) and decorrelated NPC (NPC(dec)) for the external evaluation of a population pharmacokinetic analysis, and to illustrate the use of NPDE for the evaluation of covariate models. We assume that a model M(B) has been built using a building dataset B, and that a separate validation dataset, V is available. Our null hypothesis H(0) is that the data in V can be described by M(B). We use several methods to test this hypothesis: NPDE, pd, SPE, NPC and NPC(dec). First, we evaluated by simulation the type I error under H(0) of different tests applied to the four methods. We also propose and evaluate a single global test combining normality, mean and variance tests applied to NPDE, pd and SPE. We perform tests on NPC and NPC(dec), after a decorrelation. M(B) was a one compartment model with first order absorption (without covariate), previously developed from two phase II and one phase III studies of the antidiabetic drug, gliclazide. We simulated 500 external datasets according to the design of a phase III study. Second, we investigated the application of NPDE to covariate models. We propose two approaches: the first approach uses correlation tests or mean comparisons to test the relationship between NPDE and covariates; the second evaluates NPDE split by category for discrete covariates or quantiles for continuous covariates. We generated several validation datasets under H(0) and under alternative assumptions with a model without covariate, with one continuous covariate (weight), or one categorical covariate (sex). We calculated the powers of the different tests using simulations, where the covariates of the phase III study were used. The simulations under H(0) show a high type I error for the different tests applied to SPE and an increased type I error for pd. The different tests present a type I error close to 5% for the global test appied to NPDE. We find a type I error higher than 5% for the test applied to classical NPC but this test becomes close to 5% for NPC(dec). For covariate models, when model and validation dataset are consistent, type I error of the tests are close to 5% for both effects. When validation datasets and models are not consistent, the tests detect the correlation between NPDE and the covariate. We recommend to use NPDE over SPE for external model evaluation, since they do not depend on an approximation of the model and have good statistical properties. NPDE represent a better approach than NPC, since in order to perform tests on NPC, a decorrelation step must be applied before. NPDE, in this illustration, is also a good tool to evaluate model with or without covariates.

Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R.

Pharmacokinetic/pharmacodynamic data are often analysed using nonlinear mixed-effect models, and model evaluation should be an important part of the analysis. Recently, normalised prediction distribution errors (npde) have been proposed as a model evaluation tool. In this paper, we describe an add-on package for the open source statistical package R, designed to compute npde. npde take into account the full predictive distribution of each individual observation and handle multiple observations within subjects. Under the null hypothesis that the model under scrutiny describes the validation dataset, npde should follow the standard normal distribution. Simulations need to be performed before hand, using for example the software used for model estimation. We illustrate the use of the package with two simulated datasets, one under the true model and one with different parameter values, to show how npde can be used to evaluate models. Model estimation and data simulation were performed using NONMEM version 5.1.

Are population pharmacokinetic and/or pharmacodynamic models adequately evaluated? A survey of the literature from 2002 to 2004.

Model evaluation is an important issue in population analyses. We aimed to perform a systematic review of all population pharmacokinetic and/or pharmacodynamic analyses published between 2002 and 2004 to survey the current methods used to evaluate models and to assess whether those models were adequately evaluated. We selected 324 articles in MEDLINE using defined key words and built a data abstraction form composed of a checklist of items to extract the relevant information from these articles with respect to model evaluation. In the data abstraction form, evaluation methods were divided into three subsections: basic internal methods (goodness-of-fit [GOF] plots, uncertainty in parameter estimates and model sensitivity), advanced internal methods (data splitting, resampling techniques and Monte Carlo simulations) and external model evaluation. Basic internal evaluation was the most frequently described method in the reports: 65% of the models involved GOF evaluation. Standard errors or confidence intervals were reported for 50% of fixed effects but only for 22% of random effects. Advanced internal methods were used in approximately 25% of models: data splitting was more often used than bootstrap and cross-validation; simulations were used in 6% of models to evaluate models by a visual predictive check or by a posterior predictive check. External evaluation was performed in only 7% of models. Using the subjective synthesis of model evaluation for each article, we judged the models to be adequately evaluated in 28% of pharmacokinetic models and 26% of pharmacodynamic models. Basic internal evaluation was preferred to more advanced methods, probably because the former is performed easily with most software. We also noticed that when the aim of modelling was predictive, advanced internal methods or more stringent methods were more often used.

Metrics for external model evaluation with an application to the population pharmacokinetics of gliclazide.

PURPOSE: The aim of this study is to define and illustrate metrics for the external evaluation of a population model. MATERIALS AND METHODS: In this paper, several types of metrics are defined: based on observations (standardized prediction error with or without simulation and normalized prediction distribution error); based on hyperparameters (with or without simulation); based on the likelihood of the model. All the metrics described above are applied to evaluate a model built from two phase II studies of gliclazide. A real phase I dataset and two datasets simulated with the real dataset design are used as external validation datasets to show and compare how metrics are able to detect and explain potential adequacies or inadequacies of the model. RESULTS: Normalized prediction errors calculated without any approximation, and metrics based on hyperparameters or on objective function have good theoretical properties to be used for external model evaluation and showed satisfactory behaviour in the simulation study. CONCLUSIONS: For external model evaluation, prediction distribution errors are recommended when the aim is to use the model to simulate data. Metrics through hyperparameters should be preferred when the aim is to compare two populations and metrics based on the objective function are useful during the model building process.

Absence of association between MDR1 genetic polymorphisms, indinavir pharmacokinetics and response to highly active antiretroviral therapy.

OBJECTIVE: The relationship between MDR1 single nucleotide polymorphisms (SNP) and the pharmacokinetic or pharmacodynamic responses to protease inhibitors has been recently challenged. AIM: The objective of the present study was to determine whether MDR1 genetic polymorphisms in exons 21 and 26 (G2677T/A and C3435T) are in association with indinavir (IDV) plasma concentrations and/or therapeutic response to highly active antiretroviral therapy (HAART) in HIV-infected patients treated with unboosted IDV containing regimens. METHODS: MDR1 genotyping was performed in a population of 139 HIV-1-positive patients followed during 72 weeks, as part of the previous study called ANRS 081 'Trianon'. The primary study was a randomized trial comparing over 72 weeks the efficacy of two antiretroviral drug combinations in a population of adult HIV-1-infected patients: group 1, [lamivudine (3TC) - stavudine (d4T) - IDV (800 mg three times daily)] and group 2, [Nevirapine (NVP) - d4T - IDV (1000 mg three times daily)]. RESULTS: MDR1 SNPs analyzed separately or combined into haplotypes did not show any significant association with IDV pharmacokinetics nor response to HAART. Mean modelled IDV peak and trough concentrations, as well as clearance modelled from pharmacokinetic model, after 8 weeks of therapy were not significantly different between patients carrying the wild-type haplotype GG-CC (at position 2677 and 3435 respectively) and others. CONCLUSIONS: Our results do not support an association between MDR1 genetic polymorphisms and modelled IDV clearance or clinical response to HAART.

Population pharmacokinetic analysis of indinavir in HIV-infected patient treated with a stable antiretroviral therapy.

The objectives of this study were to build a population pharmacokinetic model that describe plasma concentrations of indinavir in human immunodeficiency virus (HIV)-infected patients with sustained virological response under a stable antiretroviral combination, and to characterize the effect of covariates and co-medications on indinavir pharmacokinetics. Data were obtained from 45 patients who received different dosages of indinavir: either indinavir alone t.i.d. (mostly 800 mg), either indinavir b.i.d. (mostly 800 mg) with a booster dose of 100 mg of ritonavir. Patients were required to have a baseline plasma HIV RNA <200 copies/mL and to have unchanged antiretroviral treatment for 6 months. Indinavir concentrations were measured at a first visit (one sample before drug administration and five after) and at a second visit 3 months later (before and 1 or 3 h after drug administration). A one-compartment model with first-order absorption and first-order elimination best described indinavir pharmacokinetics. For patients treated with indinavir alone, absorption rate constant was estimated to be 0.43/h, and oral clearance Cl/F was 33 L/h. For patients treated with indinavir plus ritonavir these estimates were 0.25/h and 19 L/h, respectively. Cl/F was found to increase by 1.45-fold in men and by 1.18-fold in patients also receiving zidovudine. Oral volume of distribution (V/F) was 24 L. The inter-individual and intra-individual variability were 117 and 205% for V/F, 42 and 58% for Cl/F, respectively. This population analysis in patients with sustained virological response, quantified the effect of ritonavir on the absorption rate constant and on the clearance of indinavir, showed an increase of Cl/F in men and can be used to draw reference curve for therapeutic drug monitoring.